Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial
Open accessAbstract
BACKGROUND: Growing evidence shows that fixed, nonpersonalized daily step goals can discourage individuals, resulting in unchanged or even reduced physical activity. OBJECTIVE: The aim of this randomized controlled trial (RCT) was to evaluate the efficacy of an automated mobile phone-based personalized and adaptive goal-setting intervention using machine learning as compared with an active control with steady daily step goals of 10,000. METHODS: In this 10-week RCT, 64 participants were recruited via email announcements and were required to attend an initial in-person session. The participants were randomized into either the intervention or active control group with a one-to-one ratio after a run-in period for data collection. A study-developed mobile phone app (which delivers daily step goals using push notifications and allows real-time physical activity monitoring) was installed on each participant's mobile phone, and participants were asked to keep their phone in a pocket throughout the entire day. Through the app, the intervention group received fully automated adaptively personalized daily step goals, and the control group received constant step goals of 10,000 steps per day. Daily step count was objectively measured by the study-developed mobile phone app. RESULTS: The mean (SD) age of participants was 41.1 (11.3) years, and 83% (53/64) of participants were female. The baseline demographics between the 2 groups were similar (P>.05). Participants in the intervention group (n=34) had a decrease in mean (SD) daily step count of 390 (490) steps between run-in and 10 weeks, compared with a decrease of 1350 (420) steps among control participants (n=30; P=.03). The net difference in daily steps between the groups was 960 steps (95% CI 90-1830 steps). Both groups had a decrease in daily step count between run-in and 10 weeks because interventions were also provided during run-in and no natural baseline was collected. CONCLUSIONS: The results showed the short-term efficacy of this intervention, which should be formally evaluated in a full-scale RCT with a longer follow-up period. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02886871; https://clinicaltrials.gov/ct2/show/NCT02886871 (Archived by WebCite at http://www.webcitation.org/6wM1Be1Ng).
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In the included studies, there were positive improvements in disease management activities, such as adherence to medications and regimens.
“We also found positive improvements in disease management activities, such as adherence to medications and regimens, consistent with the existing literature20,21,83.”
From Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries (2023) · cites this paper as [83]
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Background
- Authors
- Zhou, M., Fukuoka, Y., Mintz, Y., Goldberg, K., Kaminsky, P., Flowers, E., & Aswani, A.
- Affiliations
- University of California, Berkeley (US)
- University of California, San Francisco (US)
- Corresponding author
- Aswani, Anil
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2018-01-25
- Volume
- 6
- Issue
- 1
- Pages
- e28
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC5806006
- OpenAlex
- W2785014992
- Cited by
- 111
- References
- 81
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- 14.89
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- Language
- en
- Open access
- gold
- doi.org
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- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Center for Information Technology Research in the Interest of Society
- National Institutes of Health (KL2TR000143)
- Philippine-California Advanced Research Institutes
- National Heart, Lung, and Blood Institute (R01HL104147)
- National Institute of Nursing Research (K24NR015812)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- machine learning, mobile phone app, physical activity, step count, 10,000 steps goal, push notifications, real-time activity monitoring, automated intervention, physical activity promotion, smartphone-based intervention, sedentary behavior, intervention efficacy, active control groups
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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